Abstract

Ecological communities are structured by a combination of factors known as habitat templates. These templates work as a ´filter’ that permits some species with particular traits or phenotypes to establish and persist while excluding others. Defining which habitat variables and spatial scales have the stronger influence on freshwater communities is key to effective and efficient management of fluvial ecosystems. We took advantage of the relatively recent and well-studied history of salmonid introductions to Patagonia to evaluate if patterns of association of non-native species with abiotic factors vary between different spatial scales of the environmental filter. We characterized environmental variables at the basin and reach scale to assess their influence on the presence, abundance and structure of the salmonid assemblages in breeding streams. We found no evidence supporting that presence/absence patterns of salmonid distribution were strongly driven by landscape variables, except for basins with physical barriers to migration. However, we did find support for some climatic and geomorphological variables (e.g., precipitation and relief) influencing relative abundances. Our results do not support a scenario in which the distribution of any of the species affects the distribution of the other species, and suggest interference has played only a minor role in determining current fish distribution in the region. Instead, current patterns of presence and abundance of salmonids are best explained as the product of environmental filters. Our findings contribute to our understanding of the ecology of individual species and provide insight into mechanisms structuring fish assemblages in Southern Hemisphere’s lotic systems.

Introduction

Ecological communities are structured by combinations of factors defined as habitat templates (Southwood, 1977). These habitat templates mold evolutionary forces and structure ecological strategies for each of the species within the community. In other words, both the presence of a species at a particular site and its life history strategy are the result of environmental filtering (i.e., the tolerance of the species to a particular subset of biotic and abiotic characteristics) and trade-offs met during habitat adaptation.

Fluvial ecosystems are characterized by a physical habitat strongly influenced by the inherent hierarchical structure and patchiness that determine the distribution of organisms, food availability, predation and competition (Frissell et al., 1986, Schlosser & Kallemeyn, 2000). Fine-scaled variability results from the interaction between large-scale landscape variables (e.g., basin area, slope profile of stream-associated valleys and other geomorphological traits) and smaller scale variables (e.g., local structure and condition) (Frissell et al. 1986; Schlosser & Kallemeyn 2000). Because of that, defining which habitat variables and spatial scales have the most influence on freshwater communities is key to effective and efficient management of fluvial ecosystems (Matthews, 1998, Gido et al., 2006).

Environmental filtering is considered a major structuring mechanism of communities (Weiher & Keddy 1995). It is dominated by three ecological factors: dispersal restriction, abiotic environment and biotic interactions (Belyea & Lancaster, 1999). The first two act on a regional scale and delimit the area of action of the third, which operates on a local scale (Booth & Swanton, 2006). While the utility of the environmental filtering concept has been argued on the basis that it predicts patterns that cannot be distinguished from those produced by other mechanisms, such as competition (Kraft et al., 2015), there are good reasons to explore how patterns of trait or phylogenetic dispersion change in response to the environment (Cadotte & Tucker, 2017) .Therefore, if the relative effects of these general classes of factors in streams could be disentangled, then we would gain a comprehensive view of how each factor drives community composition.

In Patagonia, particularly at the scale of large drainage basins, fish distribution has been strongly influenced by the Andean uplift and the Quaternary glacial cycles (Hubert & Renno, 2006). After the retreat of glaciers during the Pleistocene, the ability of Patagonian fish to colonize post-glacial water bodies determined their present distribution, clearly constrained by climate, especially by temperature (Cussac et al., 2004, Ruzzante et al., 2006). In recent times, native freshwater fish communities have been altered on repeated occasions by the introduction of non-native species to generate sport fisheries, resulting in communities where up to six native species and four introduced salmonid species coexist within the watershed (Macchi et al., 1999). Many of these species have stable populations at several large lakes that have been intensively studied (Cussac et al., 2014). In contrast, relatively little is known about fish assemblages in streams or about the environmental filters that structure riverine communities (Aigo et al., 2008, Pascual et al., 2002, 2007, Barriga et al.2013, Lallement et al. 2020).

Of the four species introduced since 1904, rainbow trout (Onchorhynchus mykiss, Walbaum 1792), brown trout (Salmo trutta, Linnaeus 1758) and brook trout (Salvelinus fontinalis, Mitchill 1814) are currently the most widely distributed and abundant species. The rapid adaptation of these salmonid species to the new environment enabled them to establish self-sustaining populations, obviating the need for continuous import of eggs and ensuring of a steady supply of fish from local reproducers. The short and well-known history of introductions in the region shows that these species have high dispersive capabilities and found practically no biological resistance to invasion (Pascual et al. 2007). Thus, it can be posited that their current distribution in Patagonia is the result of environmental filtering at different scales. Although the influence of the landscape scale factors has been evaluated for the two most abundant species of salmonids in previous works (Quirós, 1991, Aigo, et al., 2008; Lallement et al. 2020), it is not known whether these factors alone explain the richness of non-native species in the watershed.

The objective of this work was to examine watershed and reach-level patterns of introduced salmonid species in North Patagonian rivers and their association with watershed characteristics derived from remote sensing and topography data across an environmental gradient. We compared the relative variation in salmonid density across different kind of watersheds expecting that some environmental characteristics at a landscape and reach scale conditioned i) the presence of salmonids, ii) the abundance of each species, iii) the assemblage conformation and iv) the dominance of a species at a regional level. We characterized environmental variables at the basin and reach scale to assess their influence on the presence, abundance and structure of the salmonid assemblages in breeding streams of the Upper Limay river basin. Our findings contribute to our understanding of the ecology of individual species and provide insight into mechanisms structuring fish assemblages in Southern Hemisphere’s stream systems. Such information not only contributes to knowledge of the ecological processes structuring aquatic communities, but also helps direct conservation and management activities.

Methods

Study Area

The Upper Limay River basin is located between the provinces of Río Negro and Neuquén (~40°63’ S, 71° 70’ W), Argentina, and drains an area of 6.980 km2, most of it within the boundaries of Nahuel Huapi National Park (Figure 1). Originating in the eastern slopes of the Andes mountain range, the basin presents a complex hydrological network, with many streams, rivers and lentic water bodies. Due to rain-shadowing effects by the Andes, the area experiences a steep longitudinal climatic gradient going from 3000 mm of yearly precipitation in the West to less than 600 mm to the East in about 60 km (Paruelo et al., 1998). This climatic gradient results in an eastward transition from a cold-temperate rainforest to shrubby dry steppes. The watershed has a highly connected, complex hydrologic network characterized by deep oligotrophic lakes of varying size, interconnected by streams, ponds and wetlands. The main hub of the network is Nahuel Huapi Lake, with an area of 529 km2 and a maximum depth of 464 m, which collects most waters from the basin, and drains through the Limay River towards the Atlantic Ocean (Figure 1).

Figure 1

Selection of watersheds

Thirty-five basins fully representing the environmental gradient of the watershed were selected for this study (Figure 1). For each basin, one or more sections of 2nd order or higher were selected; selection criteria were size, registered physiographic changes, existence of natural or anthropic barriers and the accessibility to each section (Bain & Stevenson, 1999). Based on these criteria, some basins were represented by a single section, while others had two or more representative sections. On larger basins with enough altitudinal range (e.g., Machete, Gutiérrez, Ñireco, Ñirihauau and Chacabuco), additional sections on tributary streams were sampled.

Watershed variables

A total of 32 watershed attributes were chosen following the available literature (Angermeier & Winston, 1999, Oakes et al., 2005, Smith & Kraft, 2005), and were grouped into four distinct categories (Geomorphological, Climatological, Land Use and Vegetation) based on the general aspect of the environment measured by each variable (Table A-Appendix section). All categories were calculated using GIS tools (version 2.6) under the QGIS environment (QGIS Development Team, 2014), or using specific formulas following Bain and Stevenson (1999). Land Use and Vegetation data were obtained from existing digital map layers available from the National Geographic Institute, the National Institute of Agricultural Technology (INTA) and National Parks Administration’s Biodiversity Information System; these maps were complemented with satellite imagery from Google Earth. Watershed morphological variables were calculated based on a digital elevation model with a resolution of 30 m (Landsat 6TM+). Average annual precipitation (mm) and temperature were calculated based on a map interpolating averages of daily measures from 25 meteorological stations located within from 38°46’0S–41°30’0S and 70°03’0W–71°45’0W (Barros et al., 1983). The Normalized Differential Vegetation Index (NDVI) was calculated from Landsat 6TM+ satellite images during the summer season of 2014. Geoprocessing and zonal statistics were computed using Quantum Gis (QGIS) (version 2.6) and digital map data. All the variables are available in the Appendix section (Table A).

#TABLE 1 - Basin-level variables
basins <- read_csv("basins.csv")
## Rows: 35 Columns: 33
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr  (1): Basin
## dbl (32): lat, lon, area, perimeter, length, drain_net, drain_area, shape, K...
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
gt(basins)
## Warning in grepl("\n", x, fixed = TRUE): input string 1 is invalid UTF-8
`
Basin lat lon area perimeter length drain_net drain_area shape Kc relief Zmean Zmax Zmin BRR temp NDVI precip rock forest wood summit wetland urban coihue lenga steppe wetland2 highmnt TAI TAM TAR Total
Acantuco -40.6870 -71.8259 18.76 21.67 6.64 8.80 0.47 0.0106 1.40 1165 1286.18 1934 769 0.17545181 12.188889 0.22 2698.29 0.00 70.76 0.00 28.56 0.000 0.00 27.56 43.02 0.00 0.00 28.62 1.63 0.40 0.04 2.07
Blanco -40.9863 -71.7269 14.52 17.75 4.72 4.72 0.32 0.0146 1.30 1241 1410.95 2014 773 0.26292373 9.433333 0.11 2386.56 0.00 43.92 0.00 54.10 0.000 0.00 13.71 40.63 0.00 0.00 45.66 0.00 0.00 0.00 0.00
Blest -41.0244 -71.8452 12.50 20.48 6.80 10.39 0.83 0.0180 1.62 1031 1227.72 1797 766 0.15161765 13.500000 0.26 3113.83 0.00 77.32 0.00 17.29 0.000 0.00 14.08 62.48 0.00 0.00 0.00 9.50 1.60 0.32 11.43
Bonito -40.7357 -71.5788 56.72 39.23 13.24 22.74 0.40 0.0023 1.46 1147 1384.99 1916 769 0.08663142 10.427778 0.22 2001.43 0.00 73.58 0.00 26.42 0.000 0.00 20.38 38.10 0.00 0.00 28.56 14.88 0.56 0.28 15.73
Bravo -40.9687 -71.8035 46.91 38.84 10.38 12.61 0.27 0.0025 1.59 1132 1263.73 1901 769 0.10905588 11.994444 0.12 2775.10 0.00 68.04 0.00 31.95 0.000 0.00 0.00 30.31 0.00 0.00 35.96 0.00 0.00 0.00 0.00
Casa de Piedra -41.1604 -71.5157 64.78 44.05 19.77 34.13 0.53 0.0013 1.53 1457 1473.44 2206 749 0.07369752 5.550000 0.07 2324.05 0.00 58.11 10.50 30.75 0.000 0.00 0.05 35.86 0.00 0.00 37.17 11.27 0.00 0.17 11.44
Cascada -41.1561 -71.4530 12.53 20.42 7.67 7.67 0.61 0.0104 1.62 1355 1256.49 2149 794 0.17666232 16.538889 0.07 1926.90 0.00 54.93 20.67 24.37 0.000 0.00 0.00 17.56 0.00 0.00 20.43 39.70 0.00 0.00 39.70
Castilla -41.0226 -71.3418 25.76 31.00 10.27 21.49 0.83 0.0079 1.71 654 925.18 1426 772 0.06368062 23.977778 0.11 1439.01 0.00 52.79 47.02 0.00 0.209 0.00 0.00 8.85 31.37 0.00 0.00 10.01 36.48 7.72 54.21
Chacabuco -40.9954 -71.2287 134.94 70.89 25.73 68.23 0.51 0.0008 1.71 1189 1054.93 1954 765 0.04621065 18.483333 -0.02 1196.57 19.55 33.13 22.48 0.00 17.450 0.00 0.00 13.35 71.03 11.17 4.45 3.91 13.78 0.00 17.69
Challhuaco -41.2366 -71.3091 41.62 28.95 9.56 12.92 0.31 0.0034 1.26 1289 1450.07 2228 939 0.13483264 7.405556 0.06 1834.49 20.04 54.40 21.40 4.16 0.000 0.00 0.00 51.83 0.00 0.00 31.43 42.43 0.00 2.86 45.29
Coluco -40.9123 -71.6688 25.15 25.93 9.26 9.26 0.37 0.0043 1.45 1182 1438.06 1953 771 0.12764579 11.100000 0.13 2541.63 0.00 60.19 0.00 39.50 0.000 0.00 21.11 38.93 0.00 0.00 39.72 3.93 2.02 0.12 6.07
De la Quebrada -41.3616 -71.2715 14.31 18.53 5.22 5.22 0.36 0.0134 1.37 969 1635.22 2103 1134 0.18563218 2.394444 0.04 1526.00 39.39 59.59 0.00 0.00 1.000 0.00 0.00 48.36 0.00 0.00 45.56 50.00 2.14 0.43 52.56
Del Medio -41.1808 -71.2129 108.07 60.25 24.52 35.20 0.33 0.0005 1.62 935 979.40 1727 792 0.03813214 20.066667 -0.05 1248.79 0.00 0.00 25.64 1.91 1.000 0.00 0.00 7.07 72.01 4.52 1.41 20.56 5.76 0.00 26.32
Estacada -40.7830 -71.5257 49.06 35.55 13.87 27.06 0.55 0.0029 1.42 1205 1447.21 1976 771 0.08687815 9.233333 0.18 1978.71 0.00 67.66 0.00 32.33 7.820 0.10 22.85 41.70 0.00 0.00 35.45 1.88 0.00 0.00 1.88
Frey -41.1712 -71.7300 36.59 26.23 7.49 9.84 0.27 0.0048 1.21 1391 1363.49 2159 768 0.18571429 9.700000 0.20 2626.64 0.00 74.71 0.00 21.06 0.000 0.00 24.76 53.79 0.00 0.00 15.63 1.76 1.63 0.26 3.78
Gallardo -40.8701 -71.8212 97.32 52.24 17.38 32.01 0.33 0.0011 1.48 1160 1273.73 1942 782 0.06674338 10.450000 0.10 2962.76 0.00 63.91 0.00 26.78 0.704 0.00 17.70 45.91 0.00 0.00 27.51 1.75 5.50 0.00 7.25
Gutierrez -41.2067 -71.4326 160.17 63.17 28.73 60.57 0.38 0.0005 1.40 1614 1261.34 2383 769 0.05617821 11.177778 0.03 1741.80 0.00 60.22 10.85 17.55 0.000 0.00 6.07 25.99 0.00 0.00 20.90 1.05 7.64 0.00 8.69
Huemul -40.8569 -71.4419 54.14 36.52 12.80 34.12 0.63 0.0038 1.39 1371 1416.06 2140 769 0.10710938 7.027778 0.12 2112.66 0.00 64.29 0.00 35.72 0.000 0.00 26.21 29.63 0.00 0.00 40.49 7.00 0.00 0.00 7.00
Las Minas -41.2928 -71.1703 44.40 31.58 10.59 15.21 0.34 0.0031 1.33 563 1190.42 1503 940 0.05316336 16.705556 -0.04 1382.30 53.93 17.67 28.10 0.00 0.310 0.00 0.00 6.58 93.42 0.00 0.00 18.86 0.64 0.00 19.50
LLuvuco -41.1457 -71.6111 27.19 24.98 6.40 9.01 0.33 0.0081 1.34 1389 1537.49 2190 801 0.21703125 4.922222 0.04 2507.22 0.00 71.01 0.00 27.95 0.000 0.00 2.98 51.82 0.00 0.00 44.21 1.34 0.11 0.00 1.45
Machete -40.8373 -71.8332 193.67 72.96 26.40 88.88 0.46 0.0007 1.47 1190 1279.72 1945 755 0.04507576 11.605556 0.13 2942.88 0.00 63.42 0.00 28.57 3.230 0.00 19.26 43.64 0.00 0.00 32.51 4.01 4.48 0.24 8.73
Manzano-Jones -40.9812 -71.2790 30.49 37.40 13.10 13.38 0.44 0.0026 1.90 1038 1174.19 1892 854 0.07923664 16.805556 0.10 1426.93 7.37 77.96 4.13 0.00 10.550 0.00 0.00 41.52 49.49 2.33 6.69 17.00 9.46 0.00 26.46
Millaqueo -40.9743 -71.6598 52.00 42.31 16.96 35.64 0.69 0.0024 1.64 1252 1353.28 2018 766 0.07382076 11.116667 0.13 2310.23 0.00 63.32 0.00 36.67 0.000 0.00 16.13 30.13 0.00 0.00 35.83 11.01 0.00 0.35 11.36
Neuquenco -40.5768 -71.6595 21.94 26.31 6.96 7.42 0.34 0.0070 1.57 1088 1128.61 1871 783 0.15632184 16.811111 0.30 2302.93 0.00 53.04 36.24 10.73 0.000 0.00 46.17 21.10 0.00 0.00 5.52 5.16 1.54 0.09 6.78
Newbery -40.9801 -71.1867 27.83 29.27 6.56 12.97 0.47 0.0108 1.55 660 1066.44 1454 794 0.10060976 21.533333 -0.09 1137.50 60.25 0.00 19.38 0.00 19.840 0.00 0.00 0.00 100.00 0.00 0.00 0.00 0.00 0.00 0.00
搼㸱ireco -41.2078 -71.3215 113.16 62.83 19.74 37.55 0.33 0.0009 1.65 1455 1292.83 2228 773 0.07370821 10.622222 0.01 1657.58 11.90 37.37 38.55 8.73 0.000 1.58 0.00 30.10 5.34 0.00 26.90 18.80 0.09 0.00 18.89
搼㸱irihuau -41.2253 -71.1863 723.80 193.38 58.07 278.58 0.38 0.0001 2.01 1469 1191.72 2234 765 0.02529705 13.400000 -0.04 1203.16 41.89 18.99 9.30 3.60 8.940 0.02 0.00 16.92 60.04 5.48 13.08 8.92 0.54 0.00 9.47
Patiruco -41.0652 -71.7491 24.43 24.84 6.53 13.00 0.53 0.0125 1.41 1224 1271.24 2001 777 0.18744257 13.511111 0.17 2165.42 0.00 80.76 0.00 18.38 0.000 0.00 23.82 58.37 0.00 0.00 17.81 12.13 0.00 2.55 14.69
Pedregoso -40.9034 -71.3690 20.53 25.35 7.22 8.52 0.41 0.0080 1.57 1415 1567.94 2186 771 0.19598338 3.977778 0.00 1796.36 0.00 45.62 7.32 47.06 0.000 0.00 0.00 2.19 0.00 0.00 85.24 1.45 0.00 0.00 1.45
Pireco -40.7283 -71.8834 125.48 61.19 21.53 59.93 0.48 0.0010 1.53 1183 1258.26 1937 754 0.05494659 12.344444 0.19 2800.08 0.00 65.65 0.00 21.35 3.830 0.00 26.24 41.35 0.00 0.00 28.80 0.76 0.69 0.00 1.45
Quintriqueuco -40.9248 -71.3206 15.26 20.80 5.47 5.48 0.36 0.0120 1.49 1147 1526.57 1932 785 0.20968921 6.644444 0.12 1576.86 23.17 68.78 5.04 3.03 0.000 0.00 0.00 55.37 0.00 0.00 39.06 8.19 0.00 0.00 8.19
Ragintuco -40.8126 -71.4787 38.76 34.24 11.62 22.04 0.57 0.0042 1.54 1304 1460.31 2075 771 0.11222031 8.116667 0.15 2132.30 0.00 57.67 0.00 42.34 0.000 0.00 14.40 41.51 0.00 0.00 37.18 1.10 0.00 0.00 1.10
Torrontegui -41.2788 -71.4390 17.47 20.03 6.16 6.16 0.35 0.0093 1.34 1342 1622.19 2145 803 0.21785714 2.583333 0.03 1462.27 0.00 61.19 0.00 38.27 0.000 0.00 0.00 39.38 0.00 0.00 47.17 22.61 1.19 0.59 24.39
Tristeza -41.2894 -71.3209 41.29 32.45 12.16 15.88 0.38 0.0026 1.41 1164 1648.61 2234 1070 0.09572368 3.455556 -0.01 1733.11 32.36 63.47 4.19 0.00 0.000 0.00 0.00 37.54 0.48 0.00 52.97 20.56 0.32 0.32 21.19
Uhueco -41.1671 -71.6557 5.44 10.82 3.42 3.42 0.63 0.0537 1.30 1212 1530.51 2024 812 0.35438597 6.305556 0.15 2586.98 0.00 70.47 0.00 29.16 0.000 0.00 1.84 72.24 0.00 0.00 25.92 0.00 0.00 0.00 0.00

`{=html}

Local Variables

Physicochemical variables were collected in 42 reaches. Variables included geomorphological, substrate type, hydrological and water quality (Table B-Appendix section). Location and number of sections sampled by stream were determined by the pattern shape of channels, covered area and accessibility, which determined that streams with the largest area have a greater number of sections sampled. Thus, sections were selected based on particular characteristics of each stream such as changes in the relief or riverine vegetation, tributary unions and presence of ponds or waterfalls.

#TABLE 2 - Reach-level variables
reach <- read_tsv("reaches.tsv")
## Rows: 42 Columns: 36
## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr  (1): reach
## dbl (35): order, chanwidth, chandepth, bank_depth, bank_width, sinuosity, sl...
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
gt(reach)
## Warning in grepl("\n", x, fixed = TRUE): input string 1 is invalid UTF-8
`
reach order chanwidth chandepth bank_depth bank_width sinuosity slope floodwidth entrenchment width_depth_r lakekm flowlength valleylength pools wetwidth silt sand gravel cobble boulder bedrock wood velocity discharge turbidity temp cond ph tds salt benthos TAI TAM TAR Total
Acantuco 2 16.00 1.50 3.00 22.00 1.046320 0.055830144 22.00 1.3750000 10.666667 11.35 6.644130 6.35 0.20640569 8.00 0.00 70.00 10.000 10.00 10.000 2.00 9 0.24876923 303.24400 2.5066667 10.70 60.5 7.60 43.2 30.7 29.33 1.6313933 0.3968254 0.04409171 2.072310
Blest Arriba 2 12.40 1.45 2.90 19.00 1.186652 0.071875970 17.00 1.3709677 7.862069 0.65 6.799514 5.73 0.77000000 4.00 0.00 0.00 0.000 3.50 94.000 2.50 3 0.14428571 206.51750 0.7500000 10.60 33.0 7.73 23.5 18.2 750.15 13.8815442 0.1322052 0.13220518 14.145955
Bonito Toma de agua 2 15.00 1.40 2.80 15.00 1.342516 0.028977394 15.00 1.0000000 10.714286 2.40 13.237208 9.86 24.45528757 7.00 0.00 54.00 5.000 5.00 33.000 3.00 8 0.38671429 678.35000 0.4000000 7.30 57.7 8.05 40.9 26.2 108.16 12.9870130 0.7421150 0.55658627 14.285714
Bonito trampa 2 30.00 1.22 2.44 50.00 1.342516 0.037981725 50.00 1.6666667 24.590164 1.65 13.237208 9.86 0.00000000 8.00 0.00 60.00 0.000 20.00 20.000 0.00 18 0.19566667 589.24000 0.1000000 7.85 57.5 7.85 40.8 28.7 65.87 16.7910448 0.3731343 0.00000000 17.164179
Casa de Piedra Ab 3 30.70 0.80 1.60 39.00 1.198908 0.031989084 39.00 1.2703583 38.375000 3.19 19.770000 16.49 0.00000000 7.60 0.00 10.00 10.000 10.00 70.000 0.00 19 0.41033333 2742.38500 0.2200000 9.80 53.3 8.06 38.0 26.9 170.02 11.9241192 0.0000000 0.18066847 12.104788
Casa de Piedra Ar 3 19.00 0.75 1.50 27.00 1.198908 0.075854180 27.00 1.4210526 25.333333 3.78 19.770000 16.49 23.43000000 12.40 0.00 10.00 20.000 5.00 50.000 15.00 7 0.31846667 704.51000 0.2200000 9.80 53.3 8.06 38.0 26.9 72.22 10.6250000 0.0000000 0.15625000 10.781250
Castilla Abajo 4 5.76 0.93 1.86 6.17 1.202576 0.003999979 6.17 1.0711806 6.193548 0.14 10.270000 8.54 11.16279070 4.20 0.00 28.00 19.000 40.00 13.000 0.00 5 0.31057143 264.63250 1.5500000 12.40 89.3 8.04 63.3 44.5 1109.97 14.6179402 45.1827242 0.00000000 59.800664
Castilla Medio 2 4.26 0.93 1.86 78.00 1.202576 0.003999979 78.00 18.3098592 21.300000 6.73 10.270000 8.54 0.81061520 3.49 0.00 10.00 10.000 80.00 0.000 0.00 0 0.17008333 118.75437 1.3700000 13.60 127.4 7.60 90.7 63.1 1028.22 5.4027353 27.7854960 15.43638665 48.624618
Chacabuco Arriba 3 8.20 0.80 1.60 20.40 1.634708 0.001999997 20.40 2.4878049 10.250000 6.03 25.730000 21.80 20.12000000 3.00 20.00 10.00 60.000 10.00 0.000 0.00 0 0.43166667 282.77500 0.9300000 13.30 138.4 7.60 98.4 68.4 1432.33 0.7453416 11.1801242 0.00000000 11.925466
Chacabuco Des 3 7.00 0.50 1.00 50.00 1.180275 0.011999424 50.00 7.1428571 14.000000 0.10 17.050000 10.43 11.41031492 4.00 30.00 0.00 20.000 50.00 0.000 0.00 2 0.23933333 138.21750 0.8100000 17.30 134.9 7.60 96.2 68.9 1836.44 4.5641260 13.3663689 0.00000000 17.930495
Chacabuco Superior (ex manzano) 2 8.50 0.73 1.46 50.00 1.195238 0.015998635 50.00 5.8823529 12.142857 17.45 12.550000 10.50 0.00000000 2.50 85.00 0.00 15.000 0.00 0.000 0.00 11 0.04980000 16.77000 0.5900000 14.60 103.8 7.80 73.7 52.3 137.60 12.5000000 13.3333333 0.00000000 25.833333
Challhuaco Abajo 2 11.00 0.60 1.20 80.00 1.174447 0.031989084 80.00 11.4285714 18.333333 15.37 9.560000 8.14 0.00000000 8.00 5.00 15.00 35.000 35.00 10.000 0.00 0 0.34470588 491.32500 0.8100000 17.30 134.9 7.60 96.2 68.9 383.31 16.0714286 0.0000000 0.00000000 16.071429
Challhuaco Arriba 1 4.60 0.85 1.70 5.00 1.174447 0.051953207 5.00 1.0869565 5.411765 17.52 9.560000 8.14 21.82539683 2.10 5.00 5.00 40.000 40.00 10.000 0.00 9 0.39500000 254.45000 1.0200000 5.30 106.0 8.26 75.8 46.1 963.71 68.7830688 0.0000000 5.73192240 74.514991
De la Quebrada 1 7.80 0.80 1.60 7.80 1.115385 0.057935094 7.80 1.0000000 9.750000 45.86 5.220000 4.68 3.07692308 4.50 0.50 26.25 16.000 36.00 20.000 1.25 0 0.09122222 51.95250 0.1300000 9.20 134.1 8.05 95.1 63.8 531.68 50.0000000 2.1367521 0.42735043 52.564103
Del Medio 2 42.50 1.15 2.30 15.00 1.298729 0.005999928 15.00 0.3529412 36.956522 30.72 24.520000 18.88 42.76315789 2.90 0.00 60.00 8.000 30.00 2.000 0.00 1 0.00000000 0.00000 0.5000000 13.10 109.5 7.46 77.8 54.4 387.93 20.5592105 5.7565789 0.00000000 26.315789
Estacada 3 12.00 1.90 3.80 18.00 1.130399 0.051953207 16.50 1.3750000 5.947368 0.37 13.870000 12.27 0.09227683 6.80 0.00 10.00 0.000 30.00 60.000 0.00 5 0.31520000 721.44500 0.3200000 12.50 68.3 7.60 48.8 35.0 124.66 1.8750000 0.0000000 0.00000000 1.875000
Frey Abajo 2 14.00 1.50 3.00 20.00 1.044630 0.077842391 20.00 1.4285714 9.333333 0.19 7.490000 7.17 0.24315069 12.75 0.00 0.00 1.000 90.00 9.000 0.00 11 0.19516667 909.16000 1.3200000 16.80 19.3 7.58 13.7 13.7 131.94 1.7105263 1.1842105 0.26315789 3.157895
Frey Arriba 2 16.00 1.50 3.00 20.00 1.044630 0.099668652 20.00 1.2500000 10.666667 0.64 7.490000 7.17 0.00000000 12.00 0.00 0.00 10.000 0.00 0.000 90.00 0 0.19516667 909.16000 0.2600000 18.50 19.7 7.53 14.0 15.2 131.94 1.8115942 2.0703934 0.25879917 4.140787
Gallardo 2 31.00 1.60 3.20 31.00 1.203846 0.027992686 31.00 1.0000000 19.375000 1.48 18.780000 15.60 4.25000000 10.00 0.00 12.50 5.000 55.00 0.000 27.50 5 0.33020000 609.49500 0.1000000 14.20 20.9 8.80 15.0 14.2 564.00 1.7500000 5.5000000 0.00000000 7.250000
Gutierrez municipalidad (ruta) 3 14.00 1.20 2.40 100.00 1.166378 0.005999928 100.00 7.1428571 11.666667 0.41 6.730000 5.77 0.00000000 10.46 0.00 0.00 70.000 30.00 0.000 0.00 1 0.60271429 1782.51500 0.0100000 10.40 74.0 7.87 52.7 36.6 306.69 3.0729580 10.4084060 0.00000000 13.481364
Gutierrez Usina (gendarmes) 3 16.00 1.30 2.60 100.00 1.166378 0.011999424 100.00 6.2500000 12.307692 1.06 6.730000 5.77 0.00000000 5.20 0.00 70.00 10.000 20.00 0.000 0.00 1 0.23145455 809.66000 0.4500000 12.00 75.7 7.73 53.8 38.1 137.49 0.3101737 6.6687345 0.00000000 6.978908
Gutierrez Virgen 3 67.00 1.80 3.60 100.00 1.166378 0.001999997 100.00 1.4925373 37.222222 4.35 6.730000 5.77 0.00000000 16.00 10.00 30.00 20.000 40.00 0.000 0.00 0 0.26930769 1730.35500 0.6300000 13.40 72.5 7.93 51.6 37.3 222.09 0.5208333 5.9895833 0.00000000 6.510417
Huemul pcpal 3 72.00 1.50 3.00 1000.00 1.123793 0.043971638 1000.00 13.8888889 48.000000 1.59 12.800000 11.39 1.96071429 5.60 0.00 20.00 10.000 10.00 60.000 0.00 9 0.34720000 344.15500 0.1766667 10.30 63.6 7.40 45.0 31.6 6.68 4.6428571 0.0000000 0.00000000 4.642857
Huemul secundario 3 72.00 1.50 3.00 1000.00 1.123793 0.043971638 1000.00 13.8888889 48.000000 1.59 12.800000 11.39 0.00000000 3.40 0.00 5.00 75.000 15.00 5.000 0.00 2 0.33785714 283.86750 0.4300000 10.00 63.2 7.20 44.9 31.4 6.68 9.3623890 0.0000000 0.00000000 9.362389
Jones 1 5.00 0.70 1.40 12.00 1.539197 0.003999979 12.00 2.4000000 1.539197 19.13 16.100000 10.46 12.83000000 3.10 0.00 50.00 50.000 0.00 0.000 0.00 2 0.12257143 29.11000 0.4800000 13.50 92.8 7.80 66.1 46.9 80.50 21.5053763 5.5913978 0.00000000 27.096774
Las Minas 2 7.00 0.55 1.30 20.43 1.127796 0.013999085 20.43 2.9185714 12.727273 37.57 10.590000 9.39 10.54000000 0.50 26.70 40.00 6.000 6.70 20.600 0.00 7 0.26550000 11.91600 1.4800000 16.20 258.0 8.73 184.0 128.0 8159.60 37.7289377 0.7326007 0.00000000 38.461538
Lluvuco 2 16.06 1.04 2.08 16.06 1.056106 0.111535184 21.80 1.3574097 11.471429 0.30 6.400000 6.06 15.12000000 17.27 0.00 10.00 18.000 42.00 30.000 0.00 14 0.18600000 545.03500 0.0600000 10.70 23.2 7.65 16.5 13.5 143.56 1.3419216 0.1073537 0.00000000 1.449275
Machete 3 50.00 1.30 2.60 51.00 1.877667 0.005999928 51.00 1.0200000 38.461538 7.77 26.400000 14.06 0.00000000 16.00 0.00 90.00 10.000 0.00 0.000 0.00 2 0.16275000 825.39000 0.1266667 12.10 48.8 8.40 34.7 24.8 564.00 4.0094340 4.4811321 0.23584906 8.726415
Millaqueo abajo (1) 3 2.70 0.72 1.14 45.00 1.493333 0.007999829 45.00 16.6666667 3.750000 0.89 16.920000 14.15 3.61757106 17.00 0.00 40.00 1.000 39.00 20.000 0.00 1 0.40952941 1902.57500 0.1900000 9.00 25.0 8.20 18.2 13.7 118.60 8.5538626 0.0000000 0.08910274 8.642965
Millaqueo arriba (2) 3 31.50 0.60 1.20 31.50 1.195760 0.007999829 31.50 1.0000000 52.500000 1.58 16.920000 14.15 0.00000000 10.00 0.00 5.00 0.000 25.00 70.000 0.00 2 0.34970000 1576.53500 0.1500000 9.60 25.8 8.02 18.4 14.6 281.44 13.4693878 0.0000000 0.61224490 14.081633
搼㸱ireco Alto 1 6.60 0.77 1.54 8.00 1.208818 0.087773892 8.00 1.2121212 8.571429 14.90 19.740000 16.33 14.00000000 3.00 3.75 6.25 20.625 43.75 25.625 0.00 0 0.16672414 883.92250 0.1700000 8.50 81.8 8.10 58.1 37.3 146.33 16.0968661 0.0000000 0.00000000 16.096866
搼㸱ireco arriba 2 8.80 0.45 0.90 40.00 1.208818 0.011999424 40.00 4.5454545 19.555556 9.07 19.740000 16.33 3.03000000 7.70 7.50 37.50 7.500 32.50 15.000 0.00 0 0.26750000 327.90000 0.4500000 19.30 93.0 8.44 66.1 47.0 312.18 16.9696970 0.0000000 0.00000000 16.969697
搼㸱ireco Urbano 3 15.00 1.25 2.50 175.00 1.208818 0.009999667 175.00 11.6666667 12.000000 1.46 19.740000 16.33 0.00000000 8.00 2.00 5.00 13.000 15.00 65.000 0.00 0 0.43625000 714.45000 0.3600000 10.50 152.7 8.03 108.0 73.6 1815.40 18.2065217 0.2717391 0.00000000 18.478261
搼㸱irihuau Aerop 4 75.00 2.00 4.00 150.00 1.192892 0.005999928 150.00 2.0000000 37.500000 19.31 58.070000 48.68 0.00000000 19.50 2.00 40.00 4.000 4.00 50.000 0.00 0 0.21104762 2335.55000 0.6200000 8.90 72.6 8.00 51.5 35.1 127.97 2.1281124 1.5322409 0.00000000 3.702916
搼㸱irihuau alto 3 23.00 1.90 3.80 300.00 1.192892 0.033986908 300.00 13.0434783 12.105263 45.78 58.070000 48.68 18.30997151 14.40 0.00 15.00 15.000 30.00 40.000 0.00 0 0.55075000 2660.19875 0.0400000 14.30 59.1 8.25 42.0 31.5 425.35 3.1250000 0.6250000 0.00000000 3.750000
搼㸱irihuau Superior Br1 3 23.60 1.50 3.00 160.00 1.192892 0.021996452 160.00 6.7796610 15.733333 44.14 58.070000 48.68 7.60803408 10.70 0.00 5.00 20.000 15.00 60.000 0.00 0 0.55075000 2660.19875 0.0900000 6.10 56.7 7.94 40.2 25.0 76.66 11.5642118 0.3043214 0.00000000 11.868533
搼㸱irihuau superior Br2 3 13.03 1.00 2.00 160.00 1.192892 0.021996452 160.00 12.2793553 13.030000 44.14 58.070000 48.68 0.00000000 4.00 0.00 5.00 15.000 30.00 50.000 0.00 0 0.15475000 98.96125 0.0200000 8.70 66.3 7.82 45.0 30.7 72.77 23.3870968 0.0000000 0.00000000 23.387097
搼㸱irihuau Vado 3 65.00 1.60 3.20 500.00 1.192892 0.009999667 500.00 7.6923077 40.625000 32.58 58.070000 48.68 7.59000000 10.00 0.00 1.00 4.000 20.00 35.000 40.00 0 0.73938095 2848.29000 2.1100000 7.40 70.5 7.90 50.0 31.8 337.40 4.4159544 0.2374169 0.00000000 4.653371
Patiruco 2 15.40 1.27 2.54 17.40 1.070492 0.051953207 17.40 1.1298701 11.846154 0.12 6.530000 6.10 44.69987229 5.80 0.00 10.00 20.000 20.00 60.000 0.00 1 0.04545454 220.50000 0.2800000 8.90 31.9 7.60 22.3 16.6 113.22 12.1328225 0.0000000 2.55427842 14.687101
Pireco 3 33.00 1.50 3.00 70.00 1.218449 0.009999667 70.00 2.1212121 22.000000 8.61 21.530000 17.67 3.95847751 16.00 0.00 40.00 0.000 50.00 10.000 0.00 16 0.63652941 3305.80000 1.3200000 11.80 43.3 7.73 30.7 23.2 18.40 0.7612457 0.6920415 0.00000000 1.453287
Ragintuco 2 15.00 1.30 2.60 15.00 1.136986 0.091822098 15.00 1.0000000 11.538462 0.49 11.620000 10.22 0.04884319 9.00 0.00 15.00 10.000 25.00 50.000 0.00 15 0.45227273 792.99500 0.8766667 10.80 52.0 7.20 37.7 27.1 79.09 1.0958904 0.0000000 0.00000000 1.095890
Tristeza 2 6.20 0.30 0.60 7.20 1.209559 0.019997334 7.20 1.1612903 24.000000 42.08 13.160000 10.88 0.00000000 6.00 0.00 5.00 10.000 70.00 25.000 0.00 6 0.28625000 467.11750 0.4600000 5.00 85.5 7.86 60.8 38.1 192.36 20.5566097 0.3162555 0.31625553 21.189121

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Fish Capture

Different stream segments that presented a succession of pool-riffle-pool habitats were sampled during the austral summer (from December to March) of 2013-2014. The minimum sampled area depended on the size of the selected reach. In each section, presence-absence data were collected by three-pass electrofishing with a Smith-Root mod 12B equipment along 50-m reach. Relative abundance, expressed as catch per unit effort (CPUEN), was standardized based on actual length of each sweep to number of fish caught per 100 m2. Electrofishing was conducted from downstream to upstream following a zigzag trajectory and exploring all habitat types. The extremes were not blocked with nets. For basins sampled in more than one section, an average of the CPUEN of all fished sections was used as basin-level fish abundance. The Administration of Nahuel Huapi National Park and San Carlos de Bariloche Town Council approved our protocols and procedures and granted permission to collect fish samples (APN project n° 1173 and S.C de Bariloche Council note n° 412/SSMA/15).

Results

Introduced salmonid species are unevenly distributed among basins

Only two specimens of native species were caught: one of Galaxias maculatus (Jennyns, 1842) in the Frey basin and another of Hatcheria macraei (Girard, 1885) in the Ñirihuau basin. In contrast, a total of 4531 salmonids were caught. We found individuals aged between 0 and 3 years old at all sites, but older specimens (up to 8 years old) were caught only in some of the Eastern basins. No fish were captured in the Newbery, Blanco, Bravo and Uhueco basins; these streams present physical barriers (large waterfalls) restricting upstream movement of fish from the rest of the system (Figure 2).

Figure 2 Oncorhynchus mykiss was present in all basins where fish were caught. This species was also the most abundant in almost all basins (31/35); Salmo trutta, though present in 21/35 basins, was dominant in only 4 of them (Figure 2). Salvelinus fontinalis, was captured in 15/35 basins, but never dominated the assemblage (Figure 2).

Co-occurrence of introduced salmonid species varied among basins. The three species were found together in 10 basins. Only two out of three possible bispecific assemblages were found: rainbow trout with brown trout (S.trutta +O. mykiss, n = 10) and brown trout with brook trout (S.fontinalis + O.mykiss, n = 4). Monospecific assemblages were found only for rainbow trout (O. mykiss, n = 7).

No interspecific reciprocal influences on presence are evidenced by salmonid species distribution

Independence of species presence was tested using a contingency table approach, asking whether the probability of a species being present was contingent on the presence of other species.We calculated the expected frequency of each combination of species under the assumption that species distributions are independent of each other, based on the frequency each species was found. We then calculated the expected counts of all combinations, and compared them to the observed counts using a Fisher´s exact Test for contingency tables.

####Contingency test on joint species presence####

#Field data: number of basins in which a given species is present
Ntotal <- 35 #Total basins
NOm <- 31 #Basins in which rainbow trout Onchorhynchus mykiss (Om) is present
NSf <- 15 #Basins in which brook trout Salvelinus fontinalis (Sf) is present
NSt <- 21 #Basins in which brown trout Salmo trutta (St) is present

#Counts of basins in which each combination of species occur
NOb <- c(4,7,0,0,4,10,0,10)
names(NOb)<-c("none","Om","Sf","St","Om.Sf","Om.St","St.Sf","Om.Sf.St")

#Marginal probabilities
pOm <- NOm/Ntotal
pSf <- NSf/Ntotal
pSt <- NSt/Ntotal

#Calculate expected frequencies as joint probabilities
cOm <- pOm - pOm*pSf*pSt -(pOm*pSf - pOm*pSf*pSt) -(pOm*pSt - pOm*pSf*pSt)
cSf <- pSf - pOm*pSf*pSt -(pOm*pSf - pOm*pSf*pSt) -(pSt*pSf - pOm*pSf*pSt)
cSt <- pSt - pOm*pSf*pSt -(pOm*pSt - pOm*pSf*pSt) -(pSt*pSf - pOm*pSf*pSt)
cOm.Sf <- pOm*pSf - pOm*pSf*pSt
cOm.St <- pOm*pSt - pOm*pSf*pSt
cSt.Sf <- pSt*pSf - pOm*pSf*pSt
cOm.Sf.St <- pOm*pSf*pSt
none <- 1-sum(cOm,cSf,cSt,cOm.Sf,cOm.St,cSt.Sf,cOm.Sf.St)

#Build a vector of expected frequencies
frExp <- c(none,cOm,cSf,cSt,cOm.Sf,cOm.St,cSt.Sf,cOm.Sf.St)
names(frExp)<-c("none","Om","Sf","St","Om.Sf","Om.St","St.Sf","Om.Sf.St")

#Plot a Venn diagram
col.scheme <- c("red","green","blue")
draw.triple.venn(pOm,pSt,pSf,pOm*pSt,pSt*pSf,pSf*pOm,pOm*pSt*pSf, c("Om","St","Sf"), fill=col.scheme, cat.col=col.scheme, print.mode="percent")

## (polygon[GRID.polygon.11], polygon[GRID.polygon.12], polygon[GRID.polygon.13], polygon[GRID.polygon.14], polygon[GRID.polygon.15], polygon[GRID.polygon.16], text[GRID.text.17], text[GRID.text.18], text[GRID.text.19], text[GRID.text.20], text[GRID.text.21], text[GRID.text.22], text[GRID.text.23], text[GRID.text.24], text[GRID.text.25], text[GRID.text.26])
#Calculate vector of expected frequencies
NExp <- frExp*Ntotal

#Build a table of expected and observed occurrences.
expvsobs <- cbind(round(NExp),NOb)
colnames(expvsobs)<-c("Expected","Observed")
expvsobs <- as_tibble(expvsobs, rownames = "Species")
gt(expvsobs)
Species Expected Observed
none 1 4
Om 7 7
Sf 1 0
St 1 0
Om.Sf 5 4
Om.St 11 10
St.Sf 1 0
Om.Sf.St 8 10
#Performs Fisher's exact test for testing the null of independence of rows and columns in a contingency table with fixed marginals
ftest_all_basins <- fisher.test(expvsobs %>% select(Expected, Observed))
ftest_all_basins
## 
##  Fisher's Exact Test for Count Data
## 
## data:  expvsobs %>% select(Expected, Observed)
## p-value = 0.7537
## alternative hypothesis: two.sided
## Now repeat all of the above, but dropping those basins where no fish was caught.
## This explores that those basins are categorically different due to insurmountable obstacles for dispersal.
#Field data: number of basins in which a given species is present
Ntotal <- 31
NOm <- 31
NSf <- 15
NSt <- 21

#Counts of basins in which each combination of species occur
NOb <- c(7,0,0,4,10,0,10)
names(NOb)<-c("Om","Sf","St","Om.Sf","Om.St","St.Sf","Om.Sf.St")

#Marginal probabilities
pOm <- NOm/Ntotal
pSf <- NSf/Ntotal
pSt <- NSt/Ntotal

#Calculate expected frequencies as joint probabilities
cOm <- pOm - pOm*pSf*pSt -(pOm*pSf - pOm*pSf*pSt) -(pOm*pSt - pOm*pSf*pSt)
cSf <- pSf - pOm*pSf*pSt -(pOm*pSf - pOm*pSf*pSt) -(pSt*pSf - pOm*pSf*pSt)
cSt <- pSt - pOm*pSf*pSt -(pOm*pSt - pOm*pSf*pSt) -(pSt*pSf - pOm*pSf*pSt)
cOm.Sf <- pOm*pSf - pOm*pSf*pSt
cOm.St <- pOm*pSt - pOm*pSf*pSt
cSt.Sf <- pSt*pSf - pOm*pSf*pSt
cOm.Sf.St <- pOm*pSf*pSt
none <- 1-sum(cOm,cSf,cSt,cOm.Sf,cOm.St,cSt.Sf,cOm.Sf.St)

#Build a vector of expected frequencies
frExp <- c(cOm,cSf,cSt,cOm.Sf,cOm.St,cSt.Sf,cOm.Sf.St)
names(frExp)<-c("Om","Sf","St","Om.Sf","Om.St","St.Sf","Om.Sf.St")

#Plot a Venn diagram
col.scheme <- c("red","green","blue")
#draw.triple.venn(pOm,pSt,pSf,pOm*pSt,pSt*pSf,pSf*pOm,pOm*pSt*pSf, c("Om","St","Sf"), fill=col.scheme, cat.col=col.scheme, print.mode="percent")

#Calculate vector of expected frequencies
NExp <- frExp*Ntotal

#Build a table of expected and observed occurrences.

expvsobs.2 <- cbind(round(NExp),NOb) 
colnames(expvsobs.2)<-c("Expected","Observed")
expvsobs.2 <- as_tibble(expvsobs.2, rownames = "Species")
gt(expvsobs.2)
Species Expected Observed
Om 5 7
Sf 0 0
St 0 0
Om.Sf 5 4
Om.St 11 10
St.Sf 0 0
Om.Sf.St 10 10
#Performs Fisher's exact test for testing the null of independence of rows and columns in a contingency table with fixed marginals
ftest_no_empty_basins <- fisher.test(expvsobs.2 %>% select(Expected, Observed))
ftest_no_empty_basins
## 
##  Fisher's Exact Test for Count Data
## 
## data:  expvsobs.2 %>% select(Expected, Observed)
## p-value = 0.9378
## alternative hypothesis: two.sided

Fisher’s exact test failed to reject the hypothesis of independence (p = 0.753685), suggesting that species interactions are not needed to explain the frequency of each combination. Expected and observed counts are even more similar when basins without fish captures are excluded from the calculations (p = 0.9377599).

Salmonid abundances varied with environmental variables.

We then used generalized linear models (GLMs) on presence/absence and relative abundance data to assess the filtering influence of landscape and local variables. For each level and response variable type, we tried two approaches: 1) reducing the dimensionality of the independent variables through a principal components analysis (PCA), and then using the principal components explaining most variance as predictor variables in the GLM; and 2) by selecting independent variables that were not significantly correlated and using them as predictor variables in the GLM. While these two approaches are expected to yield qualitatively similar results, the second approach lends to more straightforward interpretations. All analyses were run using the function glm from the base package of the R computing environment R Core Team, version 3.5.1 (2018). To regress environmental variables using presence/absence as a response variable, we used a binomial logistic regression with a logit link. When the response variable was the logarithm of relative abundance, we used a Gaussian regression with an identity link.

#### Basin-level correlation analysis ####
basin.cpue.cor <- cor(basins[,-1], use="pairwise.complete.obs", method="spearman")[,18:21]
basin.cpue.cor.test <- rcorr(as.matrix(basins[,-1]), type="spearman") 
basin.cpue.cor.test$r[,29:32] %>%
  as_tibble(rownames = "variable")
## # A tibble: 32 x 5
##    variable       TAI     TAM    TAR   Total
##    <chr>        <dbl>   <dbl>  <dbl>   <dbl>
##  1 lat        -0.512  -0.109  -0.198 -0.538 
##  2 lon         0.434   0.0728 -0.182  0.475 
##  3 area       -0.0271  0.248  -0.221  0.0772
##  4 perimeter  -0.0273  0.304  -0.243  0.104 
##  5 length      0.0842  0.331  -0.207  0.213 
##  6 drain_net   0.0430  0.267  -0.131  0.178 
##  7 drain_area -0.0321 -0.141   0.104  0.0311
##  8 shape      -0.0847 -0.345   0.213 -0.191 
##  9 Kc          0.0955  0.187  -0.187  0.204 
## 10 relief     -0.124  -0.350  -0.129 -0.178 
## # ... with 22 more rows

Total catch of salmonids in streams from the upper Limay basin increased along a NW-SE gradient (Table 2). This gradient in relative abundance was associated to basins having a larger proportion of lowland, shrubby environments characterized by low rainfall, open woodlands and fewer rocky outcrops. This pattern in total catch was likely driven by dominance of rainbow trout, since its relative abundances presented the same pattern as the total catch (with the addition that abundance was higher at lower basins).

In contrast, brown trout abundances were not clearly associated with geographic gradients (Table 2). Instead, higher abundances were associated with lower, warmer and flatter basins, with fewer High-Andean habitat and more wetlands. Brook trout abundance showed no significant correlations with any environmental variable. However, spotty occurrence and low overall abundance of this species resulted in very low statistical power to detect any existing correlations.

Patterns of salmonid distribution are not strongly driven by landscape variables

Having ruled out a role for the interaction between species in their joint distribution (see above), we evaluated the influence of basin characteristics on individual species presence and abundances. We derived 28 watershed variables from remote sensing and topographical data sources.

#### Basin-level PCA and GLM analysis ####
basin.pca <- prcomp(basins[,2:29],center = TRUE, scale. = TRUE)
summary_basin.pca <- summary(basin.pca)
biplot(basin.pca)

basins$p.TAI <- ifelse(basins$TAI==0,0,1)
basins$p.TAR <- ifelse(basins$TAR==0,0,1)
basins$p.TAM <- ifelse(basins$TAM==0,0,1)

fviz_pca_ind(basin.pca, geom.ind = "point", 
             col.ind = "#FC4E07", 
             axes = c(1, 2), 
             pointsize = 1.5) 

fviz_screeplot(basin.pca, choice="eigenvalue")

basins <- cbind(basins, basin.pca$x[,1:3])

total.glm<-glm(Total~PC1+PC2+PC3, data=basins)
summary(total.glm)
## 
## Call:
## glm(formula = Total ~ PC1 + PC2 + PC3, data = basins)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -32.526   -6.982   -1.672    4.607   33.232  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  13.8917     2.2405   6.200 6.95e-07 ***
## PC1           1.3951     0.7351   1.898   0.0671 .  
## PC2          -2.1101     1.0755  -1.962   0.0588 .  
## PC3           2.1466     1.0898   1.970   0.0578 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 175.6907)
## 
##     Null deviance: 7437.3  on 34  degrees of freedom
## Residual deviance: 5446.4  on 31  degrees of freedom
## AIC: 285.98
## 
## Number of Fisher Scoring iterations: 2
p.tai.glm<-glm(p.TAI~PC1+PC2+PC3, data=basins, family="binomial")
summary(p.tai.glm)
## 
## Call:
## glm(formula = p.TAI ~ PC1 + PC2 + PC3, family = "binomial", data = basins)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.4523   0.1155   0.2870   0.4394   0.8824  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  3.13791    1.07382   2.922  0.00348 **
## PC1          0.43453    0.31319   1.387  0.16530   
## PC2          1.03896    0.56752   1.831  0.06715 . 
## PC3         -0.04685    0.24091  -0.194  0.84579   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 24.877  on 34  degrees of freedom
## Residual deviance: 19.421  on 31  degrees of freedom
## AIC: 27.421
## 
## Number of Fisher Scoring iterations: 7
tai.glm<-glm(TAI~PC1+PC2+PC3, data=basins)
summary(tai.glm)
## 
## Call:
## glm(formula = TAI ~ PC1 + PC2 + PC3, data = basins)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -19.3260   -6.5525    0.1208    5.3114   27.9262  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  10.6617     1.8193   5.860 1.83e-06 ***
## PC1           0.5367     0.5969   0.899  0.37549    
## PC2          -1.1018     0.8733  -1.262  0.21646    
## PC3           3.1618     0.8849   3.573  0.00118 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 115.8403)
## 
##     Null deviance: 5348.1  on 34  degrees of freedom
## Residual deviance: 3591.1  on 31  degrees of freedom
## AIC: 271.41
## 
## Number of Fisher Scoring iterations: 2
tai.glm<-glm(TAI~PC1+PC2+PC3, data=basins)
summary(tai.glm)
## 
## Call:
## glm(formula = TAI ~ PC1 + PC2 + PC3, data = basins)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -19.3260   -6.5525    0.1208    5.3114   27.9262  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  10.6617     1.8193   5.860 1.83e-06 ***
## PC1           0.5367     0.5969   0.899  0.37549    
## PC2          -1.1018     0.8733  -1.262  0.21646    
## PC3           3.1618     0.8849   3.573  0.00118 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 115.8403)
## 
##     Null deviance: 5348.1  on 34  degrees of freedom
## Residual deviance: 3591.1  on 31  degrees of freedom
## AIC: 271.41
## 
## Number of Fisher Scoring iterations: 2
log.tai.glm<-glm(log(TAI+0.00001)~PC1+PC2+PC3, data=basins)
summary(log.tai.glm)
## 
## Call:
## glm(formula = log(TAI + 1e-05) ~ PC1 + PC2 + PC3, data = basins)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -11.6393   -0.2021    1.3702    2.5841    3.6290  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.3574     0.7689   0.465    0.645
## PC1           0.1810     0.2523   0.717    0.478
## PC2           0.4053     0.3691   1.098    0.281
## PC3           0.1480     0.3740   0.396    0.695
## 
## (Dispersion parameter for gaussian family taken to be 20.69327)
## 
##     Null deviance: 680.34  on 34  degrees of freedom
## Residual deviance: 641.49  on 31  degrees of freedom
## AIC: 211.12
## 
## Number of Fisher Scoring iterations: 2
p.tam.glm<-glm(p.TAM~PC1+PC2+PC3, data=basins, family="binomial")
summary(p.tam.glm)
## 
## Call:
## glm(formula = p.TAM ~ PC1 + PC2 + PC3, family = "binomial", data = basins)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7402  -1.1886   0.7392   1.0056   1.4048  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)   0.5059     0.3826   1.322    0.186
## PC1           0.2536     0.1640   1.546    0.122
## PC2           0.1758     0.2411   0.729    0.466
## PC3          -0.1049     0.1706  -0.615    0.539
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 47.111  on 34  degrees of freedom
## Residual deviance: 43.254  on 31  degrees of freedom
## AIC: 51.254
## 
## Number of Fisher Scoring iterations: 4
tam.glm<-glm(TAM~PC1+PC2+PC3, data=basins)
summary(tam.glm)
## 
## Call:
## glm(formula = TAM ~ PC1 + PC2 + PC3, data = basins)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -11.4313   -2.5794   -0.9743    1.8485   25.0488  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   2.7591     0.9938   2.776  0.00924 **
## PC1           0.8081     0.3261   2.478  0.01885 * 
## PC2          -0.7949     0.4771  -1.666  0.10575   
## PC3          -0.9194     0.4834  -1.902  0.06650 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 34.57034)
## 
##     Null deviance: 1505.0  on 34  degrees of freedom
## Residual deviance: 1071.7  on 31  degrees of freedom
## AIC: 229.08
## 
## Number of Fisher Scoring iterations: 2
log.tam.glm<-glm(log(TAM+0.00001)~PC1+PC2+PC3, data=basins)
summary(log.tam.glm)
## 
## Call:
## glm(formula = log(TAM + 1e-05) ~ PC1 + PC2 + PC3, data = basins)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -10.422   -5.904    2.523    4.789    8.267  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -4.3252     1.0037  -4.309 0.000154 ***
## PC1           0.6148     0.3293   1.867 0.071378 .  
## PC2           0.1230     0.4818   0.255 0.800167    
## PC3          -0.4896     0.4882  -1.003 0.323693    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 35.26049)
## 
##     Null deviance: 1253.7  on 34  degrees of freedom
## Residual deviance: 1093.1  on 31  degrees of freedom
## AIC: 229.77
## 
## Number of Fisher Scoring iterations: 2
p.tar.glm<-glm(p.TAR~PC1+PC2+PC3, data=basins, family="binomial")
summary(p.tar.glm)
## 
## Call:
## glm(formula = p.TAR ~ PC1 + PC2 + PC3, family = "binomial", data = basins)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7296  -1.0883  -0.5299   1.1070   1.4891  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept) -0.42512    0.39038  -1.089   0.2762  
## PC1         -0.31573    0.17710  -1.783   0.0746 .
## PC2         -0.25317    0.25557  -0.991   0.3219  
## PC3         -0.08613    0.17121  -0.503   0.6149  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 47.804  on 34  degrees of freedom
## Residual deviance: 42.691  on 31  degrees of freedom
## AIC: 50.691
## 
## Number of Fisher Scoring iterations: 5
tar.glm<-glm(TAR~PC1+PC2+PC3, data=basins)
summary(tar.glm)
## 
## Call:
## glm(formula = TAR ~ PC1 + PC2 + PC3, data = basins)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7754  -0.5585  -0.2577   0.1115   5.9613  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.46686    0.23242   2.009   0.0533 .
## PC1          0.05132    0.07625   0.673   0.5059  
## PC2         -0.21428    0.11156  -1.921   0.0640 .
## PC3         -0.09477    0.11305  -0.838   0.4083  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 1.89062)
## 
##     Null deviance: 67.769  on 34  degrees of freedom
## Residual deviance: 58.609  on 31  degrees of freedom
## AIC: 127.37
## 
## Number of Fisher Scoring iterations: 2
log.tar.glm<-glm(log(TAR+0.00001)~PC1+PC2+PC3, data=basins)
summary(log.tar.glm)
## 
## Call:
## glm(formula = log(TAR + 1e-05) ~ PC1 + PC2 + PC3, data = basins)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -7.005  -4.859  -2.265   5.253   8.996  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -6.9855     0.9037  -7.730 1.02e-08 ***
## PC1          -0.4800     0.2965  -1.619    0.116    
## PC2          -0.3564     0.4338  -0.822    0.418    
## PC3          -0.1365     0.4396  -0.311    0.758    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 28.5864)
## 
##     Null deviance: 983.14  on 34  degrees of freedom
## Residual deviance: 886.18  on 31  degrees of freedom
## AIC: 222.43
## 
## Number of Fisher Scoring iterations: 2

We found that many of the variables were highly correlated; thus, we used a principal component analysis (PCA) to reduce the dimensionality of variable space. The first three principal components (PC) explained 65.651 % of the total variance (PC1 34.155%; PC2 15.956%, PC3 15.54%).

We used these three PCs to test if basin characteristics influenced the presence of each species by fitting a logistic regression using GLMs (see Methods), but found no significant influence of either PC on presence for any of the species. We then repeated the same analysis using relative abundance (individuals caught per unit effort; CPUEN) as a dependent variable instead. We found that rainbow trout abundance was significantly influenced by PC3 (p=0.001, loaded mainly by Latitude and Zmin), while brown trout abundance was instead, significantly influenced by PC1 (p=0.002, loaded mainly by Steppe and precipitations). Brook trout was not influenced by any PCs (Table 3). Repeating the analyses using the logarithm of relative abundance as response variable yielded no significant results for any species or PCs.

Hence, for all three species, we found that the probability of a species being present in a basin was not explained by any of the basin characteristics we used in this analysis. However, we detected evidence that the abundance of rainbow trout and brown trout (but not brook trout) is influenced by different combinations of basin traits. Thus, the three species differ in their response to landscape level filtering variables.

Local variables influence weakly but differentially the abundance of each species

To evaluate the influence of local characteristics on species presence we estimated 31 variables using local data records (see Table 3). Since several local-scale variables presented significant correlations, we used PCA to reduce the dimensionality of the variable space.

#### Reach-level PCA and GLM analysis ####
reach<-read.table("reaches.tsv", header=TRUE, sep="\t")
reach$p.TAI <- ifelse(reach$TAI==0,0,1)
reach$p.TAR <- ifelse(reach$TAR==0,0,1)
reach$p.TAM <- ifelse(reach$TAM==0,0,1)

reach.pca <- prcomp(na.omit(reach[,2:32]),center = TRUE, scale. = TRUE)
summary_reach.pca <- summary(reach.pca)
biplot(reach.pca)

fviz_pca_ind(reach.pca, geom.ind = "point", 
             col.ind = "#FC4E07", 
             axes = c(1, 2), 
             pointsize = 1.5) 

fviz_screeplot(reach.pca, choice="eigenvalue")

fviz_screeplot(reach.pca, choice="variance")

fviz_contrib(reach.pca, choice="var", axes=3)

reach <- cbind(reach, reach.pca$x[,1:3]) 

r.total.glm<-glm(Total~PC1+PC2+PC3, data=reach)
summary(r.total.glm)
## 
## Call:
## glm(formula = Total ~ PC1 + PC2 + PC3, data = reach)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -18.893   -6.282   -2.203    2.488   47.972  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  16.6030     2.0181   8.227 5.75e-10 ***
## PC1           3.9703     0.7882   5.037 1.18e-05 ***
## PC2           1.0609     0.9774   1.085    0.285    
## PC3          -0.2520     1.1941  -0.211    0.834    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 171.0609)
## 
##     Null deviance: 11049.8  on 41  degrees of freedom
## Residual deviance:  6500.3  on 38  degrees of freedom
## AIC: 340.95
## 
## Number of Fisher Scoring iterations: 2
r.p.tai.glm<-glm(p.TAI~PC1+PC2+PC3, data=reach, family="binomial")
summary(r.p.tai.glm)
## 
## Call:
## glm(formula = p.TAI ~ PC1 + PC2 + PC3, family = "binomial", data = reach)
## 
## Deviance Residuals: 
##       Min         1Q     Median         3Q        Max  
## 2.409e-06  2.409e-06  2.409e-06  2.409e-06  2.409e-06  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept)  2.657e+01  5.495e+04       0        1
## PC1         -1.717e-16  2.146e+04       0        1
## PC2          2.082e-10  2.661e+04       0        1
## PC3         -6.497e-11  3.251e+04       0        1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 0.0000e+00  on 41  degrees of freedom
## Residual deviance: 2.4367e-10  on 38  degrees of freedom
## AIC: 8
## 
## Number of Fisher Scoring iterations: 25
r.tai.glm<-glm(TAI~PC1+PC2+PC3, data=reach)
summary(r.tai.glm)
## 
## Call:
## glm(formula = TAI ~ PC1 + PC2 + PC3, data = reach)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -21.076   -6.200   -1.187    3.473   49.760  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  11.9807     1.8374   6.520  1.1e-07 ***
## PC1           2.7704     0.7176   3.861 0.000426 ***
## PC2           0.6754     0.8899   0.759 0.452562    
## PC3          -0.7988     1.0872  -0.735 0.467007    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 141.7978)
## 
##     Null deviance: 7659.9  on 41  degrees of freedom
## Residual deviance: 5388.3  on 38  degrees of freedom
## AIC: 333.07
## 
## Number of Fisher Scoring iterations: 2
r.log.tai.glm<-glm(log(TAI+0.00001)~PC1+PC2+PC3, data=reach)
summary(r.log.tai.glm)
## 
## Call:
## glm(formula = log(TAI + 1e-05) ~ PC1 + PC2 + PC3, data = reach)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -3.0062  -0.7251   0.3885   0.8647   1.9106  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.82758    0.18479   9.890 4.64e-12 ***
## PC1          0.21254    0.07217   2.945  0.00549 ** 
## PC2          0.10634    0.08949   1.188  0.24208    
## PC3         -0.05795    0.10934  -0.530  0.59918    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 1.434127)
## 
##     Null deviance: 69.363  on 41  degrees of freedom
## Residual deviance: 54.497  on 38  degrees of freedom
## AIC: 140.13
## 
## Number of Fisher Scoring iterations: 2
r.p.tam.glm<-glm(p.TAM~PC1+PC2+PC3, data=reach, family="binomial")
summary(r.p.tam.glm)
## 
## Call:
## glm(formula = p.TAM ~ PC1 + PC2 + PC3, family = "binomial", data = reach)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.7358  -1.2681   0.7601   0.8739   1.2453  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)  0.73545    0.34421   2.137   0.0326 *
## PC1          0.09223    0.14663   0.629   0.5293  
## PC2          0.11422    0.16897   0.676   0.4991  
## PC3         -0.30266    0.21264  -1.423   0.1546  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 53.467  on 41  degrees of freedom
## Residual deviance: 50.270  on 38  degrees of freedom
## AIC: 58.27
## 
## Number of Fisher Scoring iterations: 4
r.tam.glm<-glm(TAM~PC1+PC2+PC3, data=reach)
summary(r.tam.glm)
## 
## Call:
## glm(formula = TAM ~ PC1 + PC2 + PC3, data = reach)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -12.344   -3.251   -1.789    1.620   39.693  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   3.9785     1.2848   3.097  0.00367 **
## PC1           0.9957     0.5018   1.984  0.05447 . 
## PC2           0.3825     0.6222   0.615  0.54238   
## PC3           0.3544     0.7602   0.466  0.64378   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 69.3289)
## 
##     Null deviance: 2948.8  on 41  degrees of freedom
## Residual deviance: 2634.5  on 38  degrees of freedom
## AIC: 303.02
## 
## Number of Fisher Scoring iterations: 2
r.log.tam.glm<-glm(log(TAM+0.00001)~PC1+PC2+PC3, data=reach)
summary(r.log.tam.glm)
## 
## Call:
## glm(formula = log(TAM + 1e-05) ~ PC1 + PC2 + PC3, data = reach)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -9.702  -6.654   2.851   4.018   6.707  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -3.4370     0.9120  -3.769 0.000557 ***
## PC1           0.3985     0.3562   1.119 0.270279    
## PC2           0.3307     0.4417   0.749 0.458589    
## PC3          -0.6849     0.5396  -1.269 0.212100    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 34.93458)
## 
##     Null deviance: 1447.1  on 41  degrees of freedom
## Residual deviance: 1327.5  on 38  degrees of freedom
## AIC: 274.23
## 
## Number of Fisher Scoring iterations: 2
r.p.tar.glm<-glm(p.TAR~PC1+PC2+PC3, data=reach, family="binomial")
summary(r.p.tar.glm)
## 
## Call:
## glm(formula = p.TAR ~ PC1 + PC2 + PC3, family = "binomial", data = reach)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8308  -0.7637  -0.3839   0.9686   2.2445  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  -1.1141     0.5261  -2.118  0.03421 * 
## PC1           0.1992     0.2207   0.903  0.36670   
## PC2          -0.7662     0.2922  -2.622  0.00875 **
## PC3          -0.6633     0.4834  -1.372  0.17003   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 54.748  on 41  degrees of freedom
## Residual deviance: 42.257  on 38  degrees of freedom
## AIC: 50.257
## 
## Number of Fisher Scoring iterations: 6
r.tar.glm<-glm(TAR~PC1+PC2+PC3, data=reach)
summary(r.tar.glm)
## 
## Call:
## glm(formula = TAR ~ PC1 + PC2 + PC3, data = reach)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0393  -0.8905  -0.4877   0.1706  13.9396  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.642742   0.392217   1.639    0.110
## PC1         0.204868   0.153183   1.337    0.189
## PC2         0.002313   0.189950   0.012    0.990
## PC3         0.193184   0.232069   0.832    0.410
## 
## (Dispersion parameter for gaussian family taken to be 6.461028)
## 
##     Null deviance: 261.55  on 41  degrees of freedom
## Residual deviance: 245.52  on 38  degrees of freedom
## AIC: 203.35
## 
## Number of Fisher Scoring iterations: 2
r.log.tar.glm<-glm(log(TAR+0.00001)~PC1+PC2+PC3, data=reach)
summary(r.log.tar.glm)
## 
## Call:
## glm(formula = log(TAR + 1e-05) ~ PC1 + PC2 + PC3, data = reach)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.8960  -4.0011  -0.9523   3.9675  11.6834  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -7.7087     0.7528 -10.241 1.76e-12 ***
## PC1           0.1362     0.2940   0.463  0.64590    
## PC2          -1.0812     0.3646  -2.966  0.00519 ** 
## PC3          -0.2874     0.4454  -0.645  0.52270    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 23.7996)
## 
##     Null deviance: 1128.75  on 41  degrees of freedom
## Residual deviance:  904.38  on 38  degrees of freedom
## AIC: 258.11
## 
## Number of Fisher Scoring iterations: 2

The first three principal components (PC) explained 45.192 % of the total variance (PC1 21.664 %; PC2 14.089%, PC3 9.439%).

We tested whether local characteristics influenced the presence-absence of each species by fitting a logistic regression using Generalized Linear Models, and found no significant influence of either PCs on presence-absence for rainbow trout and brown trout, while PC2 (loaded mainly by water quality and slope variables) had a significant influence on presence of brook trout (z-value=-2.62, p-value=0.009). We then tested if reach variables influence relative abundances (either absolute values or their logarithms) and found a significant relation between PC1 (loaded mainly by channel morphology and water quality variables) and rainbow trout abundances (absolute – t=3.86, p=0.0004 and logarithms values –t=2.94, p=0.0055), but no significant influence on brown trout. We found significant influence of PC2 on the logarithm of brook trout abundance (t-value=-2.97, p=0.0052) but not on the absolute values. In summary, our regression analysis showed that the influence of local traits varies across all three salmonid species.

Discussion

One of the most pervasive concepts in the study of community assembly is the metaphor of the environmental filter, which refers to abiotic factors that prevent the establishment or persistence of species in a particular location. However, this concept has been criticized because the evidence used in many studies to assess environmental filtering is insufficient to distinguish filtering from the evolutionary outcome of historical biotic interactions (Kraft et al. 2015). In our work we took advantage of the relatively recent and well-studied history of salmonid introductions to evaluate if non-native species show different patterns of association with abiotic factors at different spatial scales of the filter.

Successful establishment of introduced salmonid species can depend on both biotic resistance (by the native community of organisms) and environmental resistance (habitat suitability), as well as chance events (Moyle & Light, 1996, Karr et al.,1987, Fausch,2007). The success of its dispersion has been well studied in several environments such as New Zealand (McDowall, 2003), Europe (Korsu, Huusko & Muotka, 2008) and Chile (Arismendi et al., 2014, 2019 , Habit, 2015). When salmonids were introduced to Patagonian waters in 1904 (Baigún & Quirós, 1985), they found almost no biotic resistance for the invasion and achieved a wide distribution throughout the region (Pascual et al. 2007). We indeed found widespread presence of salmonid species and almost total absence of native species in our study area. Thus, the only expected source of current biotic interference would be interspecific interaction between the salmonid species themselves. However, the results of our contingence analysis on species distribution does not support a scenario in which any of the species is affecting the distribution of the other species. This suggests that the streams in our study area have not reached carrying capacity for salmonids. Alternatively, salmonid species might be partitioning the riverine habitats to minimize niche overlap, as reported in other parts of the world (Bozek & Hubert, 1992, Reeves, Bisson & Dambacher, 1998, Fausch, 2008, Marchetti et al., 2011).

Interspecific interference among salmonid species has been proposed in previous studies in this region (Juncos, Beauchamp & Vigliano, 2013, Arismendi et al., 2014); we found however no evidence for it. Thus, we can assume that interference has played only a minor role in determining current fish distribution in the region. Instead, current patterns of presence and abundance of salmonids are best explained as the product of environmental filters. Indeed, reasons for the successful invasion reported from southern Chile has been related in part to the excellent abiotic conditions they found in the region (Pascual et al., 2005, Habit et al., 2012, Habit et al., 2015).

Biological invasions are inherently complex. A successful invader must survive a series of events: transport to the invasion site, initial establishment, spread to a broad area, and then integration into the existing biotic community (Moyle & Light, 1996, Kolar & Lodge, 2001). Not surprisingly, most introduced species fail to become established and reach invasive status (Moyle and Light 1996, Arismendi et al., 2014). Our results suggest that in North Patagonia, biotic resistance from native fish species seems to have had little or no influence on the invasion process by introduced salmonid species, and that success of invaders in the face of low odds is related, as we previously suggested, mostly with the presence of favorable environments, such as flow regime (conditioned for the rainfall gradient) and food availability (Lallement et al. 2020).

In contrast to other reports (Marchetti, Moyle & Levine, 2004, Stanfield, Scott & Borwick, 2006), we saw no evidence that presence/absence patterns of salmonid distribution were strongly driven by landscape variables, except for those basins with environmental barriers. However, when analyzing responses of relative abundances (CPUEN), the influence of climatic and geomorphological variables (e.g., precipitation and relief) became more evident. These two types of factors have been mentioned in other systems as determinants of salmonid distribution (Stanfield et al., 2006, Warren, Dunbar & Smith, 2015): rainfall and geology have a direct influence on stream discharge and are thus determinant during early development of salmonid life cycles (Heggenes & Traaen, 1988,Nehring & Anderson, 1993).

Landscape characteristics (e.g., general slope of the valley and geomorphological aspects) determine local river section traits such as substrate composition, pool dimensions and refuge availability, which in turn strongly correlate with the structure and distribution of the assemblages of fish in a basin (Fischer & Paukert, 2008,Rowe, Pierce & Wilton, 2009). For our study area, we found evidence that some local traits modulate rainbow trout abundance but do not explain abundances of brown trout and brook trout, or the presence-absence patterns for all three salmonid species. Low abundances for a species could be causing diminished statistical power to detect the influence of environmental variables; this could be affecting our results for brook trout. Differential abundances are nonetheless likely to result from differential responses to the same environment by each species. Thus, the layered influence of environmental filters was reflected in a weak but differential influence of local traits on abundance of each salmonid species.

Habitat suitability for salmonids is often controlled by local variables as temperature regime (Rahel & Nibbelink, 1999,Harig & Fausch, 2002, Coleman & Fausch, 2007), flow regime (Strange & Foin, 1999, Fausch et al., 2001), stream size (Rahel & Nibbelink, 1999) and habitat factors correlated with stream gradient and channel geomorphology (Fausch, 1989, Montgomery et al., 1999). Due to the steep precipitation gradient in our study area, several streams originate in wooded areas and cross wide valleys where shrubby vegetation typical of arid steppes predominates. Shrubby riverbanks result in higher solar irradiation and air temperatures, favoring higher primary production of the periphyton and sustaining an important biomass of macroinvertebrates (Miserendino, 2007, Modenutti et al., 2010). It is these streams where we found higher fish abundances.

A hierarchical perspective of stream systems, whereby properties at the site level are constrained by processes occurring in the catchment, provides a useful analytical framework (Vannote et al., 1980, Frissell et al., 1986, Imhol, Fitzgibbon & Annable, 1996). Previous predictive modeling studies have indicated that landscape-scale watershed characteristics are better to explain fish distributions than reach-scale characteristics (Creque, Rutherford & Zorn, 2005, Frimpong et al., 2005). However, this study did not find that watershed-scale variables were significantly better at explaining or predicting fish distributions in North Patagonia. The most likely explanation for this absence of significance can be the lack of habitat saturation by fishes due to temporal variability in fish distributions, unmeasured explanatory (reach- and watershed-scale characteristics) variables in the data set or insufficient sample size. As Stanfield and Gibson (2006), we do not contend that landscape variables are important for salmonids, but suggest that landscape traits condition the range of geomorphological variation inside a watershed that ultimately defines the current densities in a section as we previously observed (Lallement et al. 2020). Results from this study suggest further information is needed to understand how the variability of spatial scale affects the distribution of fishes in streams of Patagonia.

Conservation of stream habitats and their biota requires an understanding of how environmental factors, both natural and human-influenced, structure aquatic assemblages across different scales of space and time (Fausch, Baxter & Murakami, 2010,Grossman, Warnell & Sabo, 2010). Many previous studies have focused on the importance of variables at the local habitat scale or across relatively small study regions. Although not explored in this study, distributions of fishes are variable and may be regulated by seasonal movements and the frequency and duration of stochastic flow cycles. In any case, it is important to bear in mind that the results obtained in regions with different histories of colonization and introduction are not necessarily extrapolated. By including a comprehensive suite of variables at multiple spatial scales in a large region, our study contributes to understanding how these environmental factors may interact to structure invasive fish communities. Moreover, considering the pristine (or near-pristine) condition of the streams sampled here, the relationships observed between fishes and landscape variables can be used as a reference for further studies addressing the effects of human modifications on aquatic biodiversity of North Patagonia.

Acknowledgments

We thank to the National Park Administration (APN) that through the Biodiversity Information System (SIB) provided us with the data for Use of Land and Vegetation in watersheds. We thank all the members of the Grupo de Evaluación y Manejo de Recursos Icticos (GEMaRI) for their field assistance. Funding was provided by the Agencia Nacional de Promoción de Ciencia y Tecnología, Argentina (PICT projects 016 and 2959). The authors declare no conflict of interest.

Data Availability Statement

The data and code with the analyses supporting our findings are available at https://github.com/ezattara/patagonian_salmonid_distribution

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Session Information

sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19041)
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## [64] haven_2.4.3          splines_4.0.2        hms_1.1.0           
## [67] knitr_1.33           pillar_1.6.2         ggpubr_0.4.0        
## [70] ggsignif_0.6.2       futile.options_1.0.1 reprex_2.0.1        
## [73] glue_1.4.2           evaluate_0.14.1      latticeExtra_0.6-29 
## [76] lambda.r_1.2.4       data.table_1.14.0    modelr_0.1.8        
## [79] png_0.1-8            vctrs_0.3.8          tzdb_0.1.2          
## [82] cellranger_1.1.0     gtable_0.3.0         assertthat_0.2.1    
## [85] openxlsx_4.2.4       xfun_0.25            broom_0.7.9         
## [88] rstatix_0.7.0        cluster_2.1.2        ellipsis_0.3.2